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Updated: Sep 19, 2025

Fabrication of Electrochemical-DNA Biosensors for the Reagentless Detection of Nucleic Acids, Proteins and Small Molecules

Fabrication of Electrochemical-DNA Biosensors for the Reagentless Detection of Nucleic Acids, Proteins and Small Molecules

Published on: June 1, 2011

Machine-Learning-Aided Advanced Electrochemical Biosensors.

Andrei Bocan¹, Roozbeh Siavash Moakhar¹, Carolina Del Real Mata¹

¹Department of Bioengineering, McGill University, Montreal, Quebec, H3A 0E9, Canada.

Advanced Materials (Deerfield Beach, Fla.)

|June 9, 2025

Summary

This summary is machine-generated.

Machine learning (ML) combined with advanced nanomaterial-enhanced electrochemical biosensors significantly improves diagnostic accuracy and efficiency. This synergy addresses key challenges in biosensing, paving the way for advanced diagnostics and screening.

Keywords:

advanced materials artificial intelligence biosensors high‐throughput nanomaterials point‐of‐care wearable

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Area of Science:

Biomedical Engineering
Analytical Chemistry
Materials Science

Background:

Electrochemical biosensors offer high sensitivity, specificity, and portability.
Advanced materials and nanomaterials further enhance biosensor performance.
Machine learning (ML) shows promise in overcoming biosensor limitations like fouling and data variability.

Purpose of the Study:

To review recent applications of ML in advanced and nanomaterial-enhanced electrochemical biosensing.
To highlight the synergistic potential of combining ML with enhanced electrochemical biosensors for diagnostics and screening.

Main Methods:

Review of existing literature on ML applications in various electrochemical biosensing modalities.
Categorization of ML applications into biocatalytic, affinity-based, bioreceptor-free sensing, electrochemiluminescence, high-throughput sensing, and continuous monitoring.

Main Results:

ML effectively enhances data processing, analysis, and optimization for electrochemical biosensors.
Combined approaches show significant promise in addressing challenges like electrode fouling and sample variability.
Synergistic integration of ML with advanced/nanomaterial-enhanced biosensors is demonstrated across diverse sensing platforms.

Conclusions:

The integration of ML with advanced/nanomaterial-enhanced electrochemical biosensors offers transformative potential for diagnostics and screening.
This synergy provides a powerful toolkit for developing next-generation diagnostic and screening platforms.
Further research into their combined capabilities will accelerate advancements in the field.